2,777 research outputs found
Detecting Emerging Areas in Social Streams
Detecting the emerging areas becomes interest by the fast development of social networks. As the information exchanged in social networks post include not only the text but also images, URLs and video therefore conventional-term-frequency-based approaches may not be appropriate in this context. Emergence of areas is focused by social aspects of these networks. To detect the emergence of new areas from the hundreds of users based on the responds in social network posts. A probability model is proposed for mentioning behavior of social networks by the number of mentions per post and the occurrence of users taking place in the mentions. The basic assumption is that a new emerging topic is something people feel like discussing, stating or forwarding the data further to their friends. In the proposed system the link anomaly model is combined with word based and text based approach.
DOI: 10.17762/ijritcc2321-8169.15039
Exploiting Emergence of New Topics via Anamoly Detection: A Survey
Detecting and generating new concepts has attracted much attention in data mining era, nowadays. The emergence of new topics in news data is a big challenge. The problem can be extended as “finding breaking news”. Years ago the emergence of new stories were detected and followed up by domain experts. But manually reading stories and concluding the misbehaviors is a critical and time consuming task. Further mapping these misbehaviors to various stories needs excellent knowledge about the news and old concepts. So automatically modeling breaking news has much interest in data mining. The anomalies in news published in newspapers are the basic clues for concluding the emergence of a new story(s). The anomalies are the keywords or phrases which doesn’t match the whole concept of the news. These anomalies then processed and mapped to the stories where these keywords and phrases doesn’t behave as anomalies. After mapping these anomalies one can conclude that these mapped topic by anomaly linking can generate a new concept which eventually can be modeled as emerging story. We survey some techniques which can be used to efficiently model the new concept. News Classification, Anomaly Detection, Concept Detection and Generation are some of those techniques which collectively can be the basics of modeling breaking news. We further discussed some data sources which can process and used as input stories or news for modeling emergence of new stories
Event Detection from Social Media Stream: Methods, Datasets and Opportunities
Social media streams contain large and diverse amount of information, ranging
from daily-life stories to the latest global and local events and news.
Twitter, especially, allows a fast spread of events happening real time, and
enables individuals and organizations to stay informed of the events happening
now. Event detection from social media data poses different challenges from
traditional text and is a research area that has attracted much attention in
recent years. In this paper, we survey a wide range of event detection methods
for Twitter data stream, helping readers understand the recent development in
this area. We present the datasets available to the public. Furthermore, a few
research opportunitiesComment: 8 page
A Survey on Visual Analytics of Social Media Data
The unprecedented availability of social media data offers substantial opportunities for data owners, system operators, solution providers, and end users to explore and understand social dynamics. However, the exponential growth in the volume, velocity, and variability of social media data prevents people from fully utilizing such data. Visual analytics, which is an emerging research direction, ha..
DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion
Non-recurring traffic congestion is caused by temporary disruptions, such as
accidents, sports games, adverse weather, etc. We use data related to real-time
traffic speed, jam factors (a traffic congestion indicator), and events
collected over a year from Nashville, TN to train a multi-layered deep neural
network. The traffic dataset contains over 900 million data records. The
network is thereafter used to classify the real-time data and identify
anomalous operations. Compared with traditional approaches of using statistical
or machine learning techniques, our model reaches an accuracy of 98.73 percent
when identifying traffic congestion caused by football games. Our approach
first encodes the traffic across a region as a scaled image. After that the
image data from different timestamps is fused with event- and time-related
data. Then a crossover operator is used as a data augmentation method to
generate training datasets with more balanced classes. Finally, we use the
receiver operating characteristic (ROC) analysis to tune the sensitivity of the
classifier. We present the analysis of the training time and the inference time
separately
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